DOCSLIB.ORG

Context Based Web Indexing for Storage of Relevant Web Pages

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Context Based Web Indexing for Storage of Relevant Web Pages International Journal of Computer Applications (0975 – 8887) Volume 40– No.3, February 2012 Context based Web Indexing for Storage of Relevant Web Pages Nidhi Tyagi Rahul Rishi R.P. Agarwal Asst. Prof. Prof. & Head Prof. & Head Shobhit University, Technical Institute of Textile & Shobhit University, Meerut Sciences, Bhiwani Meerut ABSTRACT or 1. This strategy makes the searching task faster and A focused crawler downloads web pages that are relevant to a optimized. user specified topic. The downloaded documents are indexed with a view to optimize speed and performance in finding 2. RELATED WORK relevant documents for a search query at the search engine In this section, a review of previous work on index side. However, the information will be more relevant if the organization is given. In this field of index organization and context of the topic is also made available to the retrieval maintenance, many algorithms and techniques have already system. This paper proposes a technique for indexing the been proposed but they seem to be less efficient in efficiently keyword extracted from the web documents along with their accessing the index. contexts wherein it uses a height balanced binary search F. Silvestri, R.Perego and Orlando [4] proposed the (AVL) tree, for indexing purpose to enhance the performance reordering algorithm which partitions the set of documents of the retrieval system. into k ordered clusters on the basis of similarity measure where the biggest document is selected as centroid of the first General Terms cluster and n/k1 most similar documents are assigned to this Algorithm, retrieval, indexer. cluster. Then the biggest document is selected and the same process repeats. The process keeps on repeating until all the k Keywords clusters are formed and each cluster gets completed with n/k documents. This algorithm is not effective in clustering the AVL tree, contextual, repository, balance factor. most similar documents. Oren Zamir and Oren Etzioni., [5] proposed threshold 1. INTRODUCTION based clustering algorithm. Initially, the number of clusters is With the rapid growth of the Internet, the World Wide Web unknown, based on the specified threshold value the similarity (WWW) has become one of the most important resources for between the two documents is evaluated and they are obtaining information and one of the most important media of classified to the same cluster. If the threshold is small; all the communication. The basic aim is to select the best collection elements will get assigned to different clusters. If the of information according to users need. The existing focused threshold is large, the elements may get assigned to just one crawlers [1, 2] adopt different strategies for computing the cluster. Thus the algorithm is sensitive to specification of words’ frequency in the web documents. If higher frequency threshold. words match with the topic keyword, then the document is considered to be relevant. But they generally do not analyze C. Zhou, W. Ding and Na Yang [6], the paper introduces a the context of the keyword in the web page before they double indexing mechanism for search engines based on download it. The subject of context has received a great deal campus Net. The CNSE consists of crawl machine, Chinese of attention in the information recovery literature for seeking automatic segmentation, index and search machine. The relevant information [9]. Exploring the contents of the web proposed mechanism has document index as well as word pages for automatic indexing is of fundamental importance for index. The document index is based on, where the documents various web applications. The purpose of storing an index is do the clustering, and ordered by the position in each to optimize speed and performance in finding relevant document. During the retrieval, the search engine first gets the documents for a search query. Without an index, the search document id of the word in the word index, and then goes to engine would scan every document in the corpus, which the position of corresponding word in the document index. would require considerable time and computing power. For Because in the document index, the word in the same example, while an index of 10,000 documents can be queried document is adjacent, the search engine directly compares the within milliseconds, a sequential scan of every word in largest word matching assembly with the sentence that users documents is a time consuming task. The additional computer submit. The mechanism proposed, seems to be time storage required to store the index, as well as the considerable consuming as the index exists at two levels. increase in the time required for an update to take place, are N. Chauhan and A. K. Sharma [7] proposed, the context traded off for the time saved during information retrieval. driven focused crawler (CDFC) that searches and downloads In AVL (i.e. height balanced binary tree) tree [3], the height only highly relevant web pages, thus, reducing the network of a tree is defined as the length of the longest path from the traffic. A category tree has been used, which provides root node of the tree to one of its leaf node, and the balance flexibility to the user for interacting with the system showing factor (BF) is: (height of left subtree – height of right subtree). the broad categories of the topics on the web. The proposed To call the AVL as balanced the value of BF should be -1, 0 1 International Journal of Computer Applications (0975 – 8887) Volume 40– No.3, February 2012 design significantly reduces the storage space at the search engine side. P. Gupta and A. K. Sharma [8], worked on context based Crawled indexing in search engines using ontology. The index WWW web construction is done on the basis of the context using pages ontology. The context repository, thesaurus and ontology repository are used by the indexer to identify the context of the document. The critical look at the available literature reveals that there is a requirement for a technique to organize the keyword and Pre- their contexts in a better fashion as storing in a linear fashion makes searching of a document a bit time consuming. processor 3. PROPOSED WORK This paper proposes an algorithm for indexing the keyword extracted from the web documents along with their context. keywords The indexing technique uses a height balanced binary search Thesaurus (AVL) tree, in addition to improved performance in the Generate retrieval of information, this data structure is able to support dynamic indexing, which is especially important for context environments where documents are changed frequently. If the query planning about the arrangement of the keywords is done then user AVL tree can be achieved. The need to develop a technique interface that maintains a balance between the height of the left and context right sub-trees of binary tree arises for the faster search of documents stored in the database according the their keywords. AVL Query Architecture of context based indexing is represented in processor tree Figure1. The web pages are stored in the crawled web page repository. Each web document is identified by its document- id. The preprocessing steps are performed on the documents (i.e. stemming as well as removal of stop words). The frequently occurring keywords are extracted from the Keywords context Doc_id document, and their corresponding multiple contexts are identified from the thesaurus. Indexer maintains the index of the keyword using the AVL tree. The user submits the query through the search engine interface and the relevant information is made available to the user after searching the results in the index. Figure 1: Architecture of context based indexing. Figure 2 depicts the Context Based Retrieval Interface. The user enters the desired keyword (say Current), on the click of show context, the different contextual meanings of the keywords are displayed. The user selects the desired context of the keyword, through the show document button, the corresponding related web page URLs are listed (available in the repository) which can help the user to directly access more related and relevant information. This technique provides a fast access to document context and structure along with an optimized searching. 2 International Journal of Computer Applications (0975 – 8887) Volume 40– No.3, February 2012 Leftchild keyword right child link Document-Ids Context1 Context2 Context3 Figure 2: Context Based Retrieval Interface Figure 4: Node structure. 3.1 Proposed algorithm for the indexing scheme 3.2 Steps are required to construct AVL The algorithm depicted in figure 3 shows the various steps in tree the construction of the context based index and hence context (i) Creation of binary search tree: This module creates a based searching. node corresponding to the various keywords and adds the related information in the node and finds suitable location in the binary tree. Step1: Preprocess the crawled web documents and extract the keyword along with their frequency of occurrence. Create_BST() //initially the tree is empty. Step2: Input the keywords to the context generator { which extracts the multiple contextual sense of the word. create new node containing the fields ( left child, Context is being searched in the thesaurus (a dictionary keyword, rightchild, link). of words available on WWW from thesaurus.com, which Leftchild value = NULL contains the words as well their multiple meanings). Rightchild value = NULL Step3: The keywords along with the context are indexed Link = address of database where the context using the AVL tree. and the corresponding document_id is stored Step4: Compare the entered keyword with the node’s Insert_node(); keyword field of the AVL tree, until a similar word is } found. Insert_node() Step5: If search is not a success, create a node { containing the following fields (Leftchild, keyword, Check, whether value in current node and a new rightchild, link) as shown in figure4.The link is pointer keyword value are equal.
Load more

Recommended publications
  • Why We Need an Independent Index of the Web ¬ Dirk Lewandowski 50 Society of the Query Reader
    Politics of Search Engines 49 René König and Miriam Rasch (eds), Society of the Query Reader: Reections on Web Search, Amsterdam: Institute of Network Cultures, 2014. ISBN: 978-90-818575-8-1. Why We Need an Independent Index of the Web ¬ Dirk Lewandowski 50 Society of the Query Reader Why We Need an Independent Index of the Web ¬ Dirk Lewandowski Search engine indexes function as a ‘local copy of the web’1, forming the foundation of every search engine. Search engines need to look for new documents constantly, detect changes made to existing documents, and remove documents from the index when they are no longer available on the web. When one considers that the web com- prises many billions of documents that are constantly changing, the challenge search engines face becomes clear. It is impossible to maintain a perfectly complete and current index.2 The pool of data changes thousands of times each second. No search engine can keep up with this rapid pace of change.3 The ‘local copy of the web’ can thus be viewed as the Holy Grail of web indexing at best – in practice, different search engines will always attain a varied degree of success in pursuing this goal.4 Search engines do not merely capture the text of the documents they find (as is of- ten falsely assumed). They also generate complex replicas of the documents. These representations include, for instance, information on the popularity of the document (measured by the number of times it is accessed or how many links to the document exist on the web), information extracted from the documents (for example the name of the author or the date the document was created), and an alternative text-based 1.
    [Show full text]
  • Distributed Indexing/Searching Workshop Agenda, Attendee List, and Position Papers
    Distributed Indexing/Searching Workshop Agenda, Attendee List, and Position Papers Held May 28-19, 1996 in Cambridge, Massachusetts Sponsored by the World Wide Web Consortium Workshop co-chairs: Michael Schwartz, @Home Network Mic Bowman, Transarc Corp. This workshop brings together a cross-section of people concerned with distributed indexing and searching, to explore areas of common concern where standards might be defined. The Call For Participation1 suggested particular focus on repository interfaces that support efficient and powerful distributed indexing and searching. There was a great deal of interest in this workshop. Because of our desire to limit attendance to a workable size group while maximizing breadth of attendee backgrounds, we limited attendance to one person per position paper, and furthermore we limited attendance to one person per institution. In some cases, attendees submitted multiple position papers, with the intention of discussing each of their projects or ideas that were relevant to the workshop. We had not anticipated this interpretation of the Call For Participation; our intention was to use the position papers to select participants, not to provide a forum for enumerating ideas and projects. As a compromise, we decided to choose among the submitted papers and allow multiple position papers per person and per institution, but to restrict attendance as noted above. Hence, in the paper list below there are some cases where one author or institution has multiple position papers. 1 http://www.w3.org/pub/WWW/Search/960528/cfp.html 1 Agenda The Distributed Indexing/Searching Workshop will span two days. The first day's goal is to identify areas for potential standardization through several directed discussion sessions.
    [Show full text]
  • Web-Page Indexing Based on the Prioritize Ontology Terms
    Web-page Indexing based on the Prioritize Ontology Terms Sukanta Sinha1, 4, Rana Dattagupta2, Debajyoti Mukhopadhyay3, 4 1Tata Consultancy Services Ltd., Victoria Park Building, Salt Lake, Kolkata 700091, India [email protected] 2Computer Science Dept., Jadavpur University, Kolkata 700032, India [email protected] 3Information Technology Dept., Maharashtra Institute of Technology, Pune 411038, India [email protected] 4WIDiCoReL Research Lab, Green Tower, C-9/1, Golf Green, Kolkata 700095, India Abstract. In this world, globalization has become a basic and most popular human trend. To globalize information, people are going to publish the docu- ments in the internet. As a result, information volume of internet has become huge. To handle that huge volume of information, Web searcher uses search engines. The Web-page indexing mechanism of a search engine plays a big role to retrieve Web search results in a faster way from the huge volume of Web re- sources. Web researchers have introduced various types of Web-page indexing mechanism to retrieve Web-pages from Web-page repository. In this paper, we have illustrated a new approach of design and development of Web-page index- ing. The proposed Web-page indexing mechanism has applied on domain spe- cific Web-pages and we have identified the Web-page domain based on an On- tology. In our approach, first we prioritize the Ontology terms that exist in the Web-page content then apply our own indexing mechanism to index that Web- page. The main advantage of storing an index is to optimize the speed and per- formance while finding relevant documents from the domain specific search engine storage area for a user given search query.
    [Show full text]
  • Google Bing Facebook Findopen Foursquare
    The Network Google Active Monthly Users: 1B+ Today's world of smartphones, mobile moments, and self-driving cars demands accurate location data more than ever. And no single search, maps, and apps provider is more important to your location marketing strategy than Google. With the Yext Location Manager, you can manage your location data on Google My Business—the tool through which Businesses can supply data to Google Search, Google Maps, and Google+. Bing Active Monthly Users: 150M+ With more than 20% of search market share in the US and rapidly increasing traction worldwide, Bing is an essential piece of the local ecosystem. More than 150 million users search for local Businesses and services on Bing every month. Beginning in 2016, Bing will also power search results across the AOL portfolio of sites, including Huffington Post, Engadget, and TechCrunch. Facebook Active Monthly Users: 1.35B Facebook is the world’s Biggest social network with more than 2 Billion users. Yext Sync for FaceBook makes it easy to manage accurate and up-to-date contact information, photos, messages and more. FindOpen FindOpen is one of the world’s leading online Business directories and helps consumers find the critical information they need to know aBout the Businesses they’d like to visit, like hours of operation, holiday hours, special hours, menus, product and service lists, and more. FindOpen — also known as FindeOffen, TrovaAperto, TrouverOuvert, VindOpen, nyitva.hu, EncuentreAbierto, AussieHours, FindAaben, TeraZOtwarte, HittaӦppna, and deschis.ro in the many countries it operates — provides a fully responsive experience, no matter where its gloBal users are searching from.
    [Show full text]
  • Awareness Watch™ Newsletter by Marcus P
    Awareness Watch™ Newsletter By Marcus P. Zillman, M.S., A.M.H.A. http://www.AwarenessWatch.com/ V9N4 April 2011 Welcome to the V9N4 April 2011 issue of the Awareness Watch™ Newsletter. This newsletter is available as a complimentary subscription and will be issued monthly. Each newsletter will feature the following: Awareness Watch™ Featured Report Awareness Watch™ Spotters Awareness Watch™ Book/Paper/Article Review Subject Tracer™ Information Blogs I am always open to feedback from readers so please feel free to email with all suggestions, reviews and new resources that you feel would be appropriate for inclusion in an upcoming issue of Awareness Watch™. This is an ongoing work of creativity and you will be observing constant changes, constant updates knowing that “change” is the only thing that will remain constant!! Awareness Watch™ Featured Report This month’s featured report covers Deep Web Research. This is a comprehensive miniguide of reference resources covering deep web research currently available on the Internet. The below list of sources is taken from my Subject Tracer™ Information Blog titled Deep Web Research and is constantly updated with Subject Tracer™ bots at the following URL: http://www.DeepWeb.us/ These resources and sources will help you to discover the many pathways available to you through the Internet to find the latest reference deep web resources and sites. 1 Awareness Watch V9N4 April 2011 Newsletter http://www.AwarenessWatch.com/ [email protected] eVoice: 800-858-1462 © 2011 Marcus P. Zillman, M.S., A.M.H.A. Deep Web Research Bots, Blogs and News Aggregators (http://www.BotsBlogs.com/) is a keynote presentation that I have been delivering over the last several years, and much of my information comes from the extensive research that I have completed over the years into the “invisible” or what I like to call the “deep” web.
    [Show full text]
  • The Google Search Engine
    University of Business and Technology in Kosovo UBT Knowledge Center Theses and Dissertations Student Work Summer 6-2010 The Google search engine Ganimete Perçuku Follow this and additional works at: https://knowledgecenter.ubt-uni.net/etd Part of the Computer Sciences Commons Faculty of Computer Sciences and Engineering The Google search engine (Bachelor Degree) Ganimete Perçuku – Hasani June, 2010 Prishtinë Faculty of Computer Sciences and Engineering Bachelor Degree Academic Year 2008 – 2009 Student: Ganimete Perçuku – Hasani The Google search engine Supervisor: Dr. Bekim Gashi 09/06/2010 This thesis is submitted in partial fulfillment of the requirements for a Bachelor Degree Abstrakt Përgjithësisht makina kërkuese Google paraqitet si sistemi i kompjuterëve të projektuar për kërkimin e informatave në ueb. Google mundohet t’i kuptojë kërkesat e njerëzve në mënyrë “njerëzore”, dhe t’iu kthej atyre përgjigjen në formën të qartë. Por, ky synim nuk është as afër ideales dhe realizimi i tij sa vjen e vështirësohet me zgjerimin eksponencial që sot po përjeton ueb-i. Google, paraqitet duke ngërthyer në vetvete shqyrtimin e pjesëve që e përbëjnë, atyre në të cilat sistemi mbështetet, dhe rrethinave tjera që i mundësojnë sistemit të funksionojë pa probleme apo të përtërihet lehtë nga ndonjë dështim eventual. Procesi i grumbullimit të të dhënave ne Google dhe paraqitja e tyre në rezultatet e kërkimit ngërthen në vete regjistrimin e të dhënave nga ueb-faqe të ndryshme dhe vendosjen e tyre në rezervuarin e sistemit, përkatësisht në bazën e të dhënave ku edhe realizohen pyetësorët që kthejnë rezultatet e radhitura në mënyrën e caktuar nga algoritmi i Google.
    [Show full text]
  • Appendix I: Search Quality and Economies of Scale
    Appendix I: search quality and economies of scale Introduction 1. This appendix presents some detailed evidence concerning how general search engines compete on quality. It also examines network effects, scale economies and other barriers to search engines competing effectively on quality and other dimensions of competition. 2. This appendix draws on academic literature, submissions and internal documents from market participants. Comparing quality across search engines 3. As set out in Chapter 3, the main way that search engines compete for consumers is through various dimensions of quality, including relevance of results, privacy and trust, and social purpose or rewards.1 Of these dimensions, the relevance of search results is generally considered to be the most important. 4. In this section, we present consumer research and other evidence regarding how search engines compare on the quality and relevance of search results. Evidence on quality from consumer research 5. We reviewed a range of consumer research produced or commissioned by Google or Bing, to understand how consumers perceive the quality of these and other search engines. The following evidence concerns consumers’ overall quality judgements and preferences for particular search engines: 6. Google submitted the results of its Information Satisfaction tests. Google’s Information Satisfaction tests are quality comparisons that it carries out in the normal course of its business. In these tests, human raters score unbranded search results from mobile devices for a random set of queries from Google’s search logs. • Google submitted the results of 98 tests from the US between 2017 and 2020. Google outscored Bing in each test; the gap stood at 8.1 percentage points in the most recent test and 7.3 percentage points on average over the period.
    [Show full text]
  • The Indexing at the Internet
    THE INDEXING AT THE INTERNET Isidoro Gil-Leiva Universidad de Murcia Murcia/Spain ABSTRACT It is presented an analysis of the presence of indexing at the Internet supported by the idea that good part of the basic pillars in the ones which this web of computers is based is impregnated in higher or lower measure by indexing. The pillars are the metadata, the searchers, the users and the web positioning. Each one of these elements is reviewed to prove the approach which is accomplished the one is denominated Universe of the Web Indexing. Keywords: Indexing; Internet; Metadata; Web Positioning; Users; Searchers; Search Engines; Universe of Web Indexing. INTRODUCTION Before the Internet appearance, most of the occasions, the data, the information and the steps were distant and decentralized. To accomplish an administrative step it was necessary to go the administration, to read or buy a book to go to the library or to a bookstore, to contemplate determined museum work go to one, or to buy a car move to the car dealer and, like that, successively. Along with the Internet generalization almost all the activity branches of the human being were blooming on the web and, consequently, the competence of the physical world in the services, trade or culture were translated to the digital scope. However, according to the improvements of the content at the web it was necessary the development of bridges, to connect the people to the information mass available at the web, with the purpose to access in a fast and efficient way this new environment. These footbridges are the denominated search or research engines.
    [Show full text]
  • A New Hidden Web Crawling Approach
    (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 6, No. 10, 2015 A New Hidden Web Crawling Approach L.Saoudi A.Boukerram S.Mhamedi Computer science department Computer science department Computer science department Mohammed Boudiaf University Abderrahmane Mira University Mohammed Boudiaf University M‟sila, Algeria bejaia, algeria M‟sila, Algeria Abstract—Traditional search engines deal with the Surface V describes the experimental results that is done over book Web which is a set of Web pages directly accessible through domain which are discussed in section VI and finally, section hyperlinks and ignores a large part of the Web called hidden VII draws the conclusion and describes the future research. Web which is a great amount of valuable information of online database which is “hidden” behind the query forms. II. HIDDEN WEB CRAWLERS To access to those information the crawler have to fill the forms with a valid data, for this reason we propose a new A. Generality approach which use SQLI technique in order to find the most A web crawler (also known as a robot or a spider) is a promising keywords of a specific domain for automatic form system for the bulk downloading of web pages. Web crawlers submission. are used for a variety of purposes. Most prominently, they are The effectiveness of proposed framework has been evaluated one of the main components of web search engines, systems through experiments using real web sites and encouraging that assemble a corpus of web pages, index them, and allow preliminary results were obtained users to issue queries against the index and find the web pages that match the queries.
    [Show full text]
  • DEWS: a Decentralized Engine for Web Search
    DEWS: A Decentralized Engine for Web Search Reaz Ahmed∗, Md. Faizul Bari∗, Rakibul Haque∗, Raouf Boutaba∗, and Bertrand Mathieuy ∗David R. Cheriton School of Computer Science, University of Waterloo fr5ahmed | mfbari | m9haque | [email protected] yOrange Labs, Lannion, France [email protected] Abstract—Contemporary Web search is governed by centrally feasible. Thus, those solutions have issues with performance controlled search engines, which is not healthy for our online and accuracy requirements. freedom and privacy. A better solution is to enable the Web to In this paper we take a very different approach to decen- index itself in a decentralized manner. In this work we propose a decentralized Web search mechanism, named DEWS, which en- tralized web indexing and ranking. Instead of relying on an ables existing webservers to collaborate with each other to build overlay of regular Internet users, we build an overlay between a distributed index of the Web. DEWS can rank search results webservers. We exploit the stability in webserver overlay to based on query keyword relevance and relative importance of heavily cache links (network addresses) that we use as routing webpages. DEWS also supports approximate matching of query shortcuts. Thus we achieve faster lookup, lower messaging keywords in web documents. Simulation results show that the ranking accuracy of DEWS is very close to the centralized case, overhead, and higher ranking accuracy in search results. while network overhead for collaborative search and indexing is The rest of this paper is organized as follows. First we logarithmic on network size. present the DEWS architecture in xII. Then we validate the concepts presented in this work through extensive simulations and present the results in xIII.
    [Show full text]
  • Indexing the World Wide Web: the Journey So Far Abhishek Das Google Inc., USA Ankit Jain Google Inc., USA
    Indexing The World Wide Web: The Journey So Far Abhishek Das Google Inc., USA Ankit Jain Google Inc., USA ABSTRACT In this chapter, we describe the key indexing components of today’s web search engines. As the World Wide Web has grown, the systems and methods for indexing have changed significantly. We present the data structures used, the features extracted, the infrastructure needed, and the options available for designing a brand new search engine. We highlight techniques that improve relevance of results, discuss trade-offs to best utilize machine resources, and cover distributed processing concept in this context. In particular, we delve into the topics of indexing phrases instead of terms, storage in memory vs. on disk, and data partitioning. We will finish with some thoughts on information organization for the newly emerging data-forms. INTRODUCTION The World Wide Web is considered to be the greatest breakthrough in telecommunications after the telephone. Quoting the new media reader from MIT press [Wardrip-Fruin , 2003]: “The World-Wide Web (W3) was developed to be a pool of human knowledge, and human culture, which would allow collaborators in remote sites to share their ideas and all aspects of a common project.” The last two decades have witnessed many significant attempts to make this knowledge “discoverable”. These attempts broadly fall into two categories: (1) classification of webpages in hierarchical categories (directory structure), championed by the likes of Yahoo! and Open Directory Project; (2) full-text index search engines such as Excite, AltaVista, and Google. The former is an intuitive method of arranging web pages, where subject-matter experts collect and annotate pages for each category, much like books are classified in a library.
    [Show full text]
  • Introduction to Web Search Engines
    Journal of Novel Applied Sciences Available online at www.jnasci.org ©2014 JNAS Journal-2014-3-7/724-728 ISSN 2322-5149 ©2014 JNAS INTRODUCTION TO WEB SEARCH ENGINES Mohammad Reza pourmir Computer Engineering Department, Faculty of Engineering, University of zabol, Zabol, Iran Corresponding author: Mohammad Reza Pourmir ABSTRACTA: web search engine is a program designed to help find information stored on the World Wide Web. Search engine indexes are similar, but vastly more complex that back of the book indexes. The quality of the indexes, and how the engines use the information they contain, is what makes or breaks the quality of search results. The vast majority of users navigate the Web via search engines. Yet searching can be the most frustrating activity using a browser. Type in a keyword or phrase, and we're likely to get thousands of responses, only a handful of which are close to what we looking for. And those are located on secondary search pages, only after a set of sponsored links or paid-for-position advertisements. Still, search engines have come a long way in the past few years. Although most of us will never want to become experts on web indexing, knowing even a little bit about how they're built and used can vastly improve our searching skills. This paper gives a brief introduction to the web search engines, architecture, the work process, challenges faced by search engines, discuss various searching strategies, and the recent technologies in the web mining field. Keywords: Introduction, Web Search, Engines. INTRODUCTION A web search engine is a program designed to help find information stored on the World Wide Web.
    [Show full text]